A three-level framework for multi-risk assessment

The effective management of the risks posed by natural and man-made hazards requires all relevant threats and their interactions to be considered. This paper proposes a three-level framework for multi-risk assessment that accounts for possible hazard and risk interactions. The first level is a flow chart that guides the user in deciding whether a multi-hazard and risk approach is required. The second level is a semi-quantitative approach to explore if a more detailed, quantitative assessment is needed. The third level is a detailed quantitative multi-risk analysis based on Bayesian networks. Examples that demonstrate the application of the method are presented.     

A Bayesian network approach for predicting seismic liquefaction based on interpretive structural modeling

The Bayesian network (BN) is a type of graphical network based on probabilistic inference that has been gradually applied to assessment of seismic liquefaction potential. However, how to construct a robust BN remains underexplored in this field. This paper aims to present an efficient hybrid approach combining domain knowledge and data to construct a BN that facilitates the integration of multiple factors and the quantification of uncertainties within a network model to assess seismic liquefaction. Initially, only using given domain knowledge, a naive network model can be constructed using interpretive structural modeling. Thereafter, some effective information about the naive model is provided to construct a robust model using structural learning of BN from historic data. Finally, the returning predictive results and the predictive results are compared to other methods including non-probabilistic and probabilistic models for seismic liquefaction using the metrics of the overall accuracy, the area under the curve of receiver operating characteristic, prediction, recall and F₁ score. The methodology proposed in this paper achieved better performance, and we discussed the power and value of the proposed approach at the end of this paper, which suggest that BN is a good alternative tool for seismic liquefaction prediction.     

面向室内WLAN定位的动态自适应模型

提出了一种面向无线局域网(wireless local area network,WLAN)位置指纹匹配定位的动态自适应模型,借助多个基站的实时数据为稳健室内定位提供更新的匹配数据库——radio map。基于基站与radio map采样点之间的空间关联性,将基站数据和radio map分别作为多层神经网络的输入和输出,动态更新radio map;利用多元数据异常探测技术检验基站数据捕获环境的时空变化;根据探测结果采用顾及室内布局的数值内插和再训练的方式更新模型,从而使其适应环境的时空变化。在室内动态环境中进行了多次验证试验。试验结果显示,在时变因素作用下,相比较传统方法,采用所提模型的定位方法的平均误差至少下降10%;在空间变化因素(以信标移动为例)作用下,其他方法平均定位误差大幅增加了至少165%,而采用所提模型方法的平均定位误差只增加了10%~20%,定位精度维持在3m左右(即原始精度)。结果证明采用了所提模型的定位系统能够自适应环境的时空变化而保持原有定位表现。不过,与传统位置指纹匹配定位方法相比,该模型带来了更多的计算负荷。     

大型高精度GNSS基线向量网并行抗差估计

针对大型GNSS基线向量网的特点,在改进的相关观测抗差估计RECO方案的基础上采用并行计算技术进行相关抗差估计的并行计算(简称"并行抗差估计"),并给出了可行的解算步骤。通过算例分析,验证了大型GNSS基线向量网的并行相关抗差估计,不仅有效抑制了观测异常对参数估值的影响,而且显著提高了计算效率。     

基于CORS站的GPS平面控制网起算点兼容性研究

不同等级或者不同时期布设控制点都可能存在较大误差,如果以误差较大点做起算,都会扭曲GPS测量的精度,严重影响GPS成果可靠性。文中以某城市四等GPS控制网为例,探讨以城市CORS站分析GPS控制网起算点兼容性,并得到成功应用。     

采用弱基准和抗差估计的重力网平差

以沿海某测区绝对重力和相对重力网数据处理为例,详细描述了弱基准重力网平差的方法,绝对重力和相对重力先验权的确定,用抗差等价权来调整相对重力的权、重力仪参数的取舍。结果表明,弱基准能有效地提高整网的精度;抗差估计能有效地探测并降低异常数据的权。重力点平均中误差为13.6×10-8 ms-2,偶然误差检验符合正态分布。     

基于小波分析的灰色-神经网络组合模型在大坝变形预测中的应用

监测序列经小波分解后可以得到各层分量。对低频分量采用灰色GM(1,1)模型进行建模预测,对高频分量采用BP神经网络进行建模预测,最后将各分量进行小波重构,得到监测序列的预测值。将预测值分别与没有进行小波分解直接用GM(1,1)模型预测的值和经小波分解的低、高频系数都采用GM(1,1)模型预测的值进行对比,发现经小波分解的灰色-神经网络组合模型预测精度更高。     

引汉济渭黄三段控制网布设及数据处理研究

针对引汉济渭工程控制网布设、观测和数据处理等具体问题,该文结合引汉济渭黄三段施工控制网的建立,研究了大型复杂工程施工控制网的布设方案、观测和数据处理方式以及验证控制网可靠性的做法,阐述了项目的作业流程、主要方法、关键技术和成果精度,对类似项目的生产具有指导意义和推广价值。     

Context-aware abstraction and generalization of street networks: two cognitively engineered user-oriented approaches using network Voronoi diagrams

Generalization and spatial contextual awareness are prevalent concepts in geographic information systems. This study adopted a context-dependent user-centred network generalization method to create a sub-network for optimal route finding. The results show an increase in the number of preferred traversed edges. A route presentation approach is suggested that is based on cognitively engineered user-oriented abstraction of street network. Successive abstractions hierarchize the street network to create a hierarchical presentation structure. The route is projected onto one level in the structure. Then, instead of showing the exact route, the network Voronoi regions represented by the projected route are shown. Experience indicates that the suggested method is an efficient way of route presentation for a hierarchical regionalized structure of a human cognitive map.     

Comparison of basin morphometry derived from topographic maps,ASTER and SRTM DEMs: an example from Kerala,India

The drainage network of a sixth-order tropical river basin, viz. Ithikkara river basin, was extracted from different sources such as Survey of India topographic maps (1: 50,000; TOPO) and digital elevation data of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (30 m) and Shuttle Radar Topography Mapping Mission (SRTM) (90 m). Basin morphometric attributes were estimated to evaluate the accuracy of the digital elevation model (DEM)-derived drainage networks for hydrologic applications as well as terrain characterization. The stream networks derived from ASTER and SRTM DEMs show significant agreement (with slight overestimation of lower order streams) with that of TOPO. The study suggests that SRTM (despite the coarser spatial resolution) provides better results, in drainage delineation and basin morphometry, compared to ASTER. Further, the variability of basin morphometry among the data sources might be attributed to spatial variation of elevation, raster grid size and vertical accuracy of the DEMs as well as incapability of the surface hydrologic analysis functions in the GIS platform.